Week 3 HW: OPENTRONS

Homework 3:

Python Script for Opentrons Artwork

Overview

The task is to create a Python script that runs on an Opentrons OT-2 liquid handling robot and draws an artistic design on a 96-well plate using dye transfer. The design chosen for this project is a mercury droplet, representing the core element of the MeR (Mercury Bioremediation) project. Adapted to reflect a droplet / letter “M” for Mercurio.

Labware

Slot	Labware
1	USA Scientific 96-well deep plate (2.4 mL)
2	Opentrons 96-tip rack (300 µL)
3	NEST 12-well reservoir (15 mL)

Python Script — `MeR_art.py`

from opentrons import protocol_api
 
metadata = {
    'protocolName': 'MeR Art — Mercury Bioremediation',
    'author': 'Violeta Vilcapoma Torres',
    'description': 'Mercury droplet drawing via dye transfer on a 96-well plate',
    'apiLevel': '2.13'
}
 
def run(protocol: protocol_api.ProtocolContext):
 
    # --- Load labware ---
    plate     = protocol.load_labware('usascientific_96_wellplate_2.4ml_deep', '1')
    tiprack   = protocol.load_labware('opentrons_96_tiprack_300ul', '2')
    reservoir = protocol.load_labware('nest_12_reservoir_15ml', '3')
 
    # --- Load pipette ---
    p300 = protocol.load_instrument('p300_single_gen2', 'left', tip_racks=[tiprack])
 
    # --- Well coordinates: mercury droplet / "M" shape ---
    # Generated via opentrons-art.rcdonovan.com GUI
    mercury_drop = ['D4', 'E4', 'F5', 'G6', 'F7', 'E8', 'D8', 'C7', 'B6', 'C5']
 
    silver_dye = reservoir.wells_by_name()['A1']  # Silver/gray dye in reservoir well A1
 
    # --- Drawing routine ---
    p300.pick_up_tip()
    for well in mercury_drop:
        p300.distribute(
            20,                              # 20 µL per well
            silver_dye,
            plate.wells_by_name()[well],
            new_tip='never'                  # single tip for the full drawing pass
        )
    p300.drop_tip()

POST LAB QUESTIONS

Paper: Accelerated high-throughput imaging and phenotyping system for small organisms

Link: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0287739

This paper details the creation of a high-throughput experimentation (HTE) platform built around duckweeds — specifically Lemna minor, a tiny aquatic plant with applications in bioremediation and biofuel research. To run large-scale evolutionary ecology experiments, the team combined an Opentrons OT-2 liquid handling robot with a custom autonomous imaging system, creating a pipeline capable of operating at a scale that would be practically impossible by hand. The central engineering challenge was that standard liquid handling robots are designed to work with, unsurprisingly, liquids. Duckweeds are solid floating plant fronds, which meant the OT-2 needed to be rethought for a very different kind of material. The researchers solved this by replacing the standard pipette tips on the OT-2’s P300 pipette heads with commercial inoculation loops. These loops exploit capillary action to gently lift individual fronds from the water’s surface, allowing the robot to pick and place solid biological matter with the same reliability it would otherwise bring to liquid transfers. This seemingly simple hardware modification had enormous practical consequences. By enabling automated handling of the plants, the team was able to design an experiment encompassing 6,000 individual microcosms spread across 2,000 distinct combinations of nutrients and microbes — a scale of experimental complexity that manual pipetting and plant placement could never realistically achieve, given how tedious and error-prone working with tiny floating organisms at high volume would be for human researchers.

Question 2 — Describe what you intend to automate for your final project

Project: High-throughput screening of stabilized MerA enzyme variants for mercury bioremediation.

For my final project, I would like to automate the screening of different computationally designed MerA enzyme variants to identify which one shows the best stability and activity under mercury stress conditions.

The main idea is to test several MerA variants in parallel, instead of doing each assay manually. This would help reduce pipetting errors, save time, and allow the comparison of many conditions within a single working day.

Planned Automation Workflow

Step 1 — Sample preparation

First, an automated liquid-handling robot, such as the Opentrons OT-2, would prepare the samples. The robot would mix different concentrations of HgCl₂ with cell lysates containing each MerA variant. This step is important because it allows the same mercury stress conditions to be applied consistently across all samples.

Step 2 — Thermal stability screening

Then, the samples would be exposed to controlled temperature conditions using a temperature module, such as an Inheco module. This would allow me to evaluate how stable each MerA variant remains after thermal stress. During this stage, the robot could also add the required cofactors, such as NADPH and FAD, at defined time points.

Step 3 — Activity readout

After incubation, the samples would be transferred to a 384-well plate and analyzed using a plate reader, such as the PHERAstar. The readout would be based on absorbance changes related to the reduction of Hg²⁺ to Hg⁰, which would indicate the activity level of each MerA variant.

Pseudocode (something like this jejje)

for variant in stabilized_protein_list:
    robot.transfer(HgCl2_solution, assay_plate)      # add toxic mercury
    robot.transfer(variant_MerA,   assay_plate)      # add enzyme variant
    robot.mix(repetitions=5, volume=50, location=assay_plate)
    inheco.incubate(temp=37, duration_min=30)        # thermal stress
    plate_reader.measure_absorbance(assay_plate)     # quantify activity